CN111179520B - Telecommunication fraud early warning method, system and storage medium - Google Patents
Telecommunication fraud early warning method, system and storage medium Download PDFInfo
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- CN111179520B CN111179520B CN201911241517.9A CN201911241517A CN111179520B CN 111179520 B CN111179520 B CN 111179520B CN 201911241517 A CN201911241517 A CN 201911241517A CN 111179520 B CN111179520 B CN 111179520B
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07F—COIN-FREED OR LIKE APPARATUS
- G07F19/00—Complete banking systems; Coded card-freed arrangements adapted for dispensing or receiving monies or the like and posting such transactions to existing accounts, e.g. automatic teller machines
- G07F19/20—Automatic teller machines [ATMs]
- G07F19/207—Surveillance aspects at ATMs
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- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
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- G07F—COIN-FREED OR LIKE APPARATUS
- G07F19/00—Complete banking systems; Coded card-freed arrangements adapted for dispensing or receiving monies or the like and posting such transactions to existing accounts, e.g. automatic teller machines
- G07F19/20—Automatic teller machines [ATMs]
- G07F19/209—Monitoring, auditing or diagnose of functioning of ATMs
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Abstract
The invention discloses a telecommunication fraud early warning method, a telecommunication fraud early warning system and a storage medium, wherein the method comprises the following steps: collecting basic data of face pictures of money drawing personnel in real time at an ATM (automatic teller machine) network; analyzing the basic data of the face picture of the withdrawing person, and acquiring the abnormal behavior of the withdrawing person; when the abnormal behavior is detected, performing historical behavior detection on the withdrawing personnel, and analyzing the characteristics of the abnormal behavior; comparing the basic data of the face picture of the teller with the data of a distribution control database, and performing real-time early warning according to a comparison result; and updating the information of the deployment and control database. The invention collects and captures the withdrawal records and facial picture information of the withdrawers, judges whether the withdrawers have abnormal behaviors and analyzes the characteristics of the abnormal behaviors, and gives corresponding early warning, thereby realizing the early warning of telecommunication fraud in the ATM withdrawal link and improving the early warning accuracy rate to a certain extent compared with the traditional method. The method can be widely applied to the technical field of anti-telecommunication fraud.
Description
Technical Field
The invention relates to the technical field of anti-telecommunication fraud, in particular to a telecommunication fraud early warning method, a telecommunication fraud early warning system and a storage medium.
Background
In recent years, with the development of internet, means of telecommunication network fraud are in a wide range, in the link of anti-telecommunication fraud, as fraud methods and means are different, it is difficult to effectively capture and judge the fraud of forepart criminal suspects, there is no effective means to prevent and stop the occurrence of telecommunication fraud activities, and the prevention can only be realized by improving the anti-fraud consciousness of the masses of people.
According to incomplete statistics, more than 85% of economic crimes are drawn by using an ATM (automatic teller machine), and a criminal suspect of telecom fraud finally draws money through the ATM to cash out illegal results. Therefore, attention needs to be paid to and the links of taking cash from an ATM (automatic teller machine) by criminal suspects are fully utilized, behaviors of the teller are analyzed and early warned, and accurate defense and early warning is implemented on the 'last kilometer' of telecommunication fraud. However, an effective telecommunication fraud early warning method aiming at the ATM (automatic teller machine) withdrawal link is still lacked at present, and the monitoring strength and the early warning strategy of telecommunication fraud illegal criminal behaviors are still insufficient.
Disclosure of Invention
In view of the above, the present invention is directed to an effective method, system and storage medium for telecommunication fraud early warning.
The invention provides a telecommunication fraud early warning method, which comprises the following steps:
collecting basic data of face pictures of money drawing personnel in real time at an ATM (automatic teller machine) network;
analyzing the basic data of the face picture of the withdrawing person, and acquiring the abnormal behavior of the withdrawing person;
when the abnormal behavior is detected, performing historical behavior detection on the withdrawing personnel, and analyzing the characteristics of the abnormal behavior;
comparing the basic data of the face picture of the teller with the data of a distribution control database, and performing real-time early warning according to a comparison result;
and updating the information of the deployment and control database.
Further, the step of collecting basic data of face pictures of the money withdrawers at the ATM network point in real time comprises the following steps:
acquiring face information and sending the face information to a back end;
acquiring real-time basic data of the withdrawing personnel;
and inputting the basic data of the withdrawing personnel into a basic database.
Further, the abnormal behavior information includes an eye-shielding portion, a head-shielding portion, and a face-shielding portion.
Further, the step of detecting the historical behavior of the teller and analyzing the abnormal behavior characteristics when the abnormal behavior is detected comprises the following steps:
acquiring a drawing history of the drawing personnel from a basic database to form track information of the drawing personnel;
and analyzing abnormal behavior characteristics of the track information, and carrying out early warning according to an analysis result.
Further, the step of obtaining the drawing history of the drawing personnel from the basic database to form the track information of the drawing personnel comprises the following steps:
acquiring the face characteristic value of the withdrawing person, traversing a basic database, and acquiring the record of the withdrawing person;
and sequencing the records of the withdrawing personnel according to time to form a historical withdrawing track of the withdrawing personnel.
Further, the abnormal behavior characteristics comprise a first abnormal behavior characteristic, a second abnormal behavior characteristic, a third abnormal behavior characteristic and a fourth abnormal behavior characteristic;
the first abnormal behavior feature is expressed by the formula:
wherein the content of the first and second substances,
a probability of a presence of a first abnormal behavior feature for the drawing person, n being a number of trajectories of the drawing person,
for the time hour value of the ith trace of the payee,
is the starting time for the night time determination,
is the end time of the night decision;
the second abnormal behavior feature is expressed by the formula:
wherein the content of the first and second substances,
the similarity of the second abnormal behavior characteristics exists for the drawing personnel, n is the track number of the drawing personnel,
a collection of locations for the trajectory of the dispensing person,
the number of elements in the set is,
is a correction factor;
the third abnormal behavior characteristic is represented by the formula:
wherein the content of the first and second substances,
there is a similarity of third unusual behavior features for the drawing staff,
indicating that whether the track set T of the day of the withdrawal person is judged to be multi-site withdrawal or not,
for the location track set of the ith day in the withdrawal person track,
representing the number of elements in the set T, m representing the total number of days of the trajectory of the drawer,
determining a value for multi-location withdrawal;
the fourth abnormal behavior feature is expressed by the formula:
wherein the content of the first and second substances,
the fact that the drawing staff has a fourth abnormal behavior characteristic set is shown, m is the number of days when the behavior of the drawing staff changes, namely, the drawing staff does not have drawing records in 0-m days and has drawing records in m-n days, or the drawing staff has drawing records in 0-m days and has no drawing records in m-n days, n is the number of days of the drawing staff track,
is the trace set of the drawing person on the ith day.
Further, the step of updating the deployment and control database information includes the following steps:
detecting data stored in a deployment and control database, and acquiring data for eliminating fraud suspicions;
removing the obtained fraud suspicion excluded data from the deployment database;
and acquiring the released telecommunication fraud information and storing the information in a control database.
The invention also provides a telecommunication fraud early warning system, which comprises:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement a telecommunications fraud pre-warning method as described above.
The invention also provides a telecommunication fraud early warning system, which comprises:
the data acquisition module is used for acquiring basic data of facial pictures of the money withdrawers at the ATM (automatic teller machine) network in real time;
the real-time behavior analysis module is used for analyzing the basic data of the face picture of the real-time money withdrawing person and obtaining abnormal behavior information of the real-time money withdrawing person;
the historical behavior analysis module is used for detecting the historical behavior of the real-time money withdrawing personnel when the abnormal behavior information is detected;
the comparison early warning module is used for comparing the basic data of the facial picture of the teller with the data of the distribution control database and carrying out real-time early warning according to the comparison result;
the control database updating module updates the control database information;
the control database is used for storing face information of the criminal suspects in the telecommunication fraud active service;
and the basic database is used for storing basic data of the withdrawing personnel.
The present invention also provides a storage medium having stored therein processor-executable instructions, which when executed by a processor, are used for performing the telecommunication fraud early warning method as described above.
One or more of the above-described embodiments of the present invention have the following advantages: the invention collects the basic data of the real-time face pictures of the withdrawing personnel at the ATM network, comprehensively captures the withdrawing records of the withdrawing personnel, processes the face picture information of the withdrawing personnel at the ATM, judges whether the withdrawing personnel have abnormal behaviors and analyzes the abnormal behavior characteristics of the withdrawing personnel, and processes the early warning and the report information of the withdrawing personnel with the abnormal behavior characteristics, thereby realizing the early warning of the telecom fraud and illegal criminal behaviors in the ATM withdrawing link and improving the early warning accuracy rate to a certain extent compared with the traditional method.
Drawings
FIG. 1 is a flow chart of a telecommunication fraud early warning method of the present invention;
FIG. 2 is a schematic structural diagram of a telecommunication fraud early warning system of the present invention.
Detailed Description
The invention will be further explained and explained with reference to the drawings and the embodiments in the description. The step numbers in the embodiments of the present invention are set for convenience of illustration only, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.
Referring to fig. 1, a telecommunication fraud early warning method includes the following steps:
s1 collecting basic data of face pictures of ATM nodes for real-time money drawing personnel;
s2, analyzing the basic data of the face picture of the withdrawing person and acquiring the abnormal behavior of the withdrawing person;
s3, when the abnormal behavior is detected, carrying out historical behavior detection on the withdrawing personnel, and analyzing the characteristics of the abnormal behavior;
s4, comparing the basic data of the face picture of the teller with the data of a distribution control database, and performing real-time early warning according to the comparison result;
s5 updates the deployment database information.
As a preferred embodiment, the method S1 for acquiring real-time data specifically includes the following steps:
a face snapshot camera is deployed in the ATM, when a face is detected, automatic snapshot is carried out, and the picture information and the snapshot time and position information are transmitted to a back-end processing system.
And after receiving the face picture, the rear end calculates the characteristic value of the face by adopting a face algorithm and inputs the characteristic value and the time and position information into the withdrawal personnel information base DB 1.
As an alternative embodiment, the following method may also be adopted to obtain the face snapshot picture:
and when the ATM system detects that a withdrawal operation exists, the camera issues a snapshot instruction position, and the camera realizes snapshot and returns related information.
The information entered into the basic database comprises the following information: the method comprises the following steps of drawing money, drawing money time, drawing money place, drawing money face picture characteristic value, drawing money face picture and other information.
As a preferred embodiment, in the step of S2 analyzing the basic data of the facial image of the teller and acquiring an abnormal behavior of the teller, the abnormal behavior refers to a behavior of taking money from night to early morning and covering the head and the face with a helmet, a hat, sunglasses, a mask, etc., and the facial and head features of the teller are identified by using an image identification technology and a face identification and detection algorithm.
In a preferred embodiment, in the step of S3, when the abnormal behavior is detected, performing historical behavior detection on the teller and analyzing abnormal behavior characteristics, according to the picture information of the teller, a historical withdrawal record set of the teller is found from the basic database, and the historical behavior characteristics of the target are analyzed from three dimensions of time, place and frequency.
The abnormal behavior characteristics comprise a first abnormal behavior characteristic, a second abnormal behavior characteristic, a third abnormal behavior characteristic and a fourth abnormal behavior characteristic, wherein the first abnormal behavior characteristic is as follows: frequently withdrawals at night or in the morning, the second abnormal behavior is characterized by: withdrawal frequently at multiple locations, a third exception behavior characterized by: withdrawals are made at multiple locations throughout the day, and the fourth abnormal behavior is characterized by: withdrawal is frequently made for a period of time and no withdrawal information is made for a period of time.
The method for acquiring the historical historic sites of the withdrawers comprises the following steps: and calculating the face characteristic value of the withdrawing person, traversing a basic database, removing records with the face characteristic value similar to the face characteristic value of the withdrawing person by more than 80%, and sequencing according to time.
The first abnormal behavior feature is expressed by the formula:
wherein the content of the first and second substances,
a probability of a presence of a first abnormal behavior feature for the drawing person, n being a number of trajectories of the drawing person,
for the time hour value of the ith trace of the payee,
is the starting time for the night time determination,
is the end time of the night decision; in this example
The value of the oxygen is 0, and the oxygen concentration is less than or equal to zero,
a value of 6 when
If the number is greater than 0.7, the withdrawal person is considered to have the first abnormal behavior characteristic.
The second abnormal behavior feature is expressed by the formula:
wherein the content of the first and second substances,
the similarity of the second abnormal behavior characteristics exists for the drawing personnel, n is the track number of the drawing personnel,
a collection of locations for the trajectory of the dispensing person,
the number of elements in the set is,
is a correction factor and has a value range of 1 to 1.5, including 1 and 1.5, in this embodimentIs 1.3;
the third abnormal behavior characteristic is represented by the formula:
wherein the content of the first and second substances,
there is a similarity of third unusual behavior features for the drawing staff,
indicating that whether the track set T of the day of the withdrawal person is judged to be multi-site withdrawal or not,
for the location track set of the ith day in the withdrawal person track,
representing the number of elements in the set T, m representing the total number of days of the trajectory of the drawer,
determine value for multi-location withdrawal when
The drawing staff can be considered to have the third abnormal behavior characteristic when in use, wherein
A decision threshold for a third abnormal behavior feature; in the present embodiment, it is preferred that,
the value of the number is 3,
the value is 0.5.
The fourth abnormal behavior feature is expressed by the formula:
wherein the content of the first and second substances,
the fact that the drawing staff has a fourth abnormal behavior characteristic set is shown, m is the number of days when the behavior of the drawing staff changes, namely, the drawing staff does not have drawing records in 0-m days and has drawing records in m-n days, or the drawing staff has drawing records in 0-m days and has no drawing records in m-n days, n is the number of days of the drawing staff track,
is the trace set of the drawing person on the ith day.
As a preferred embodiment, in S4, the comparison between the basic data of the facial image of the teller and the data in the control database is performed by calculating a face feature value in real-time data through a face algorithm, then sequentially removing the face feature value information in the control database, and calculating the similarity between the face feature value information and the face feature value information through a face matching algorithm. And if the similarity is greater than a given threshold value, giving an alarm in real time. Wherein the setting of the threshold value in the present embodiment is 80%.
In a preferred embodiment, in the step of updating the deployment database information in S5, when the early-warning person is confirmed for the second time and no suspicion of the telecommunication fraud is determined, the deployment information corresponding to the person is deleted from the deployment database. And if the public security department publishes the information of the new telecommunication fraud personnel, the information of the personnel is recorded into the control database.
The invention also discloses a telecommunication fraud early warning system, which comprises:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement a telecommunications fraud pre-warning method as described above.
Referring to fig. 2, the present invention also discloses a telecommunication fraud early warning system, comprising:
the data acquisition module is used for acquiring basic data of facial pictures of the money withdrawers at the ATM (automatic teller machine) network in real time;
the real-time behavior analysis module is used for analyzing the basic data of the face picture of the real-time money withdrawing person and obtaining abnormal behavior information of the real-time money withdrawing person;
the historical behavior analysis module is used for detecting the historical behavior of the real-time money withdrawing personnel when the abnormal behavior information is detected;
the comparison early warning module is used for comparing the basic data of the facial picture of the teller with the data of the distribution control database and carrying out real-time early warning according to the comparison result;
the control database updating module updates the control database information;
the control database is used for storing face information of the criminal suspects in the telecommunication fraud active service;
and the basic database is used for storing basic data of the withdrawing personnel.
The present invention also discloses a storage medium having stored therein processor-executable instructions, which when executed by a processor, are used for performing the telecommunication fraud early warning method as described above.
In summary, compared with the prior art, the invention has the following advantages:
(1) the invention collects the basic data of the real-time face pictures of the withdrawing personnel at the ATM network, comprehensively captures the withdrawing records of the withdrawing personnel, processes the face picture information of the withdrawing personnel at the ATM, judges whether the withdrawing personnel have abnormal behaviors and analyzes the abnormal behavior characteristics of the withdrawing personnel, and processes the early warning and the report information of the withdrawing personnel with the abnormal behavior characteristics, thereby realizing the early warning of the telecom fraud and illegal criminal behaviors in the ATM withdrawing link and improving the early warning accuracy rate to a certain extent compared with the traditional method.
(2) The invention adopts the face recognition algorithm to analyze the basic data of the face picture of the teller, and obviously saves the labor cost because the full-automatic processing of the computer technology can be adopted.
(3) The method combines real-time behavior analysis and historical behavior analysis to analyze the abnormal behavior of the drawing staff, and the combined analysis can further narrow the range of criminal suspects, help to improve the early warning accuracy rate and lock telecommunication fraud criminal suspects.
(4) The invention can update the information of the deployment and control database, and continuously self-feed back and adjust to optimize the deployment and control database, thereby continuously improving the accuracy and efficiency of early warning.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A telecommunication fraud early warning method is characterized by comprising the following steps:
collecting basic data of face pictures of money drawing personnel in real time at an ATM (automatic teller machine) network;
analyzing the basic data of the face picture of the withdrawing person, and acquiring the abnormal behavior of the withdrawing person;
when the abnormal behavior is detected, performing historical behavior detection on the withdrawing personnel, and analyzing the characteristics of the abnormal behavior;
comparing the basic data of the face picture of the teller with the data of a distribution control database, and performing real-time early warning according to a comparison result;
updating the information of the deployment and control database;
the abnormal behavior characteristics comprise a first abnormal behavior characteristic, a second abnormal behavior characteristic, a third abnormal behavior characteristic and a fourth abnormal behavior characteristic;
the first abnormal behavior feature is expressed by the formula:
wherein the content of the first and second substances,
a probability of a presence of a first abnormal behavior feature for the drawing person, n being a number of trajectories of the drawing person,
for the time hour value of the ith trace of the payee,
is the starting time for the night time determination,
is the end time of the night decision;
the second abnormal behavior feature is expressed by the formula:
wherein the content of the first and second substances,
the similarity of the second abnormal behavior characteristics exists for the drawing personnel, n is the track number of the drawing personnel,
a collection of locations for the trajectory of the dispensing person,
the number of elements in the set is,
is a correction factor;
the third abnormal behavior characteristic is represented by the formula:
wherein the content of the first and second substances,
there is a similarity of third unusual behavior features for the drawing staff,
indicating that whether the track set T of the day of the withdrawal person is judged to be multi-site withdrawal or not,
for the location track set of the ith day in the withdrawal person track,
representing the number of elements in the set T, m representing the total number of days of the trajectory of the drawer,
determining a value for multi-location withdrawal;
the fourth abnormal behavior feature is expressed by the formula:
wherein the content of the first and second substances,
the fact that the drawing staff has a fourth abnormal behavior characteristic set is shown, m is the number of days when the behavior of the drawing staff changes, namely, the drawing staff does not have drawing records in 0-m days and has drawing records in m-n days, or the drawing staff has drawing records in 0-m days and has no drawing records in m-n days, n is the number of days of the drawing staff track,
is the trace set of the drawing person on the ith day.
2. The telecommunication fraud early-warning method of claim 1, wherein: the step of collecting basic data of face pictures of real-time withdrawing people at an ATM (automatic teller machine) network point comprises the following steps:
acquiring face information and sending the face information to a back end;
acquiring real-time basic data of the withdrawing personnel;
and inputting the basic data of the withdrawing personnel into a basic database.
3. The telecommunication fraud early-warning method of claim 1, wherein: the abnormal behavior information includes an eye-shielding portion, a head-shielding portion, and a face-shielding portion.
4. The telecommunication fraud early-warning method of claim 2, wherein: when the abnormal behavior is detected, the step of detecting the historical behavior of the withdrawing personnel and analyzing the characteristics of the abnormal behavior comprises the following steps:
acquiring a drawing history of the drawing personnel from a basic database to form track information of the drawing personnel;
and analyzing abnormal behavior characteristics of the track information, and carrying out early warning according to an analysis result.
5. The telecommunication fraud early-warning method of claim 4, wherein: the step of obtaining the drawing history of the drawing personnel from the basic database and forming the track information of the drawing personnel comprises the following steps:
acquiring the face characteristic value of the withdrawing person, traversing a basic database, and acquiring the record of the withdrawing person;
and sequencing the records of the withdrawing personnel according to time to form a historical withdrawing track of the withdrawing personnel.
6. The telecommunication fraud early-warning method of claim 1, wherein: the step of updating the information of the deployment and control database comprises the following steps:
detecting data stored in a deployment and control database, and acquiring data for eliminating fraud suspicions;
removing the obtained fraud suspicion excluded data from the deployment database;
and acquiring the released telecommunication fraud information and storing the information in a control database.
7. A telecommunications fraud early warning system, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement a telecommunications fraud pre-warning method as recited in any of claims 1-6.
8. A storage medium having stored therein processor-executable instructions, wherein the processor-executable instructions, when executed by a processor, are for performing the telecommunication fraud early warning method recited in any one of claims 1-6.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101794481A (en) * | 2009-02-04 | 2010-08-04 | 深圳市先进智能技术研究所 | ATM (Automatic teller machine) self-service bank monitoring system and method |
| CN103246869A (en) * | 2013-04-19 | 2013-08-14 | 福建亿榕信息技术有限公司 | Crime monitoring method based on face recognition technology and behavior and sound recognition |
| CN104657817A (en) * | 2015-01-28 | 2015-05-27 | 四川君逸易视科技有限公司 | Face snapshotting, comparing, identifying, retrieving, and inquiring method for bank counter |
| CN103226694B (en) * | 2013-03-28 | 2016-06-22 | 赵福辉 | A kind of portrait in real time obtains comparison and early warning cloth Ore-controlling Role and using method thereof |
| CN103714631B (en) * | 2013-12-26 | 2016-07-06 | 苏州清研微视电子科技有限公司 | ATM cash dispenser intelligent monitor system based on recognition of face |
| CN109785207A (en) * | 2017-11-15 | 2019-05-21 | 娄奥林 | A kind of ways and means of crime prevention prediction discovery |
| WO2019217538A1 (en) * | 2018-05-11 | 2019-11-14 | Diebold Nixdorf Incorporated | Method of operating an automated transaction machine for enhanced security |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10395226B2 (en) * | 2014-01-31 | 2019-08-27 | Ncr Corporation | Maintaining secure access to a self-service terminal (SST) |
| KR20190044997A (en) * | 2017-10-23 | 2019-05-02 | 효성티앤에스 주식회사 | Kiosk apparatus and method for operating the same |
-
2019
- 2019-12-06 CN CN201911241517.9A patent/CN111179520B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101794481A (en) * | 2009-02-04 | 2010-08-04 | 深圳市先进智能技术研究所 | ATM (Automatic teller machine) self-service bank monitoring system and method |
| CN103226694B (en) * | 2013-03-28 | 2016-06-22 | 赵福辉 | A kind of portrait in real time obtains comparison and early warning cloth Ore-controlling Role and using method thereof |
| CN103246869A (en) * | 2013-04-19 | 2013-08-14 | 福建亿榕信息技术有限公司 | Crime monitoring method based on face recognition technology and behavior and sound recognition |
| CN103714631B (en) * | 2013-12-26 | 2016-07-06 | 苏州清研微视电子科技有限公司 | ATM cash dispenser intelligent monitor system based on recognition of face |
| CN104657817A (en) * | 2015-01-28 | 2015-05-27 | 四川君逸易视科技有限公司 | Face snapshotting, comparing, identifying, retrieving, and inquiring method for bank counter |
| CN109785207A (en) * | 2017-11-15 | 2019-05-21 | 娄奥林 | A kind of ways and means of crime prevention prediction discovery |
| WO2019217538A1 (en) * | 2018-05-11 | 2019-11-14 | Diebold Nixdorf Incorporated | Method of operating an automated transaction machine for enhanced security |
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